Evaluation of Unsolicited Feedback from Patients with Cancer and Their Families as a Strategy to Improve Cancer Care Delivery
Bibliographic record
Abstract
BACKGROUND: Unsolicited patient feedback (compliments and complaints) should allow the healthcare system to address and improve individual and overall patient, family, and staff experiences. We evaluated feedback at a tertiary cancer centre to identify potential areas for optimizing care delivery. METHODS: unsolicited feedback submitted to the Patient Relations Department, relating to the Divisions of Medical and Radiation Oncology, at the Ottawa Hospital, was analyzed. RESULTS: Of 580 individual reports submitted from 2016 to 2022, patient demographics were available for 97% (563/580). Median patient age was 65 years (range 17-101), and 53% (301/563) were female. The most common cancer types were breast (127/545, 23%) and gastrointestinal (119/545, 22%) malignancies, and most (64%, 311/486) patients had metastatic disease. Feedback was submitted mainly by patients (291/579, 50%), and predominantly negative (489/569, 86%). The main reasons for complaints included: communication (29%, 162/566) and attitude/conduct of care (28%, 159/566). While feedback rates were initially stable, an increase occurred from 2019 to 2021. CONCLUSIONS: Unsolicited feedback remains mostly negative, and relates to physician communication. If we are to drive meaningful changes in care delivery, more standardized means of assessing feedback and implementation strategies are needed. In addition, in an era of increased healthcare provider burnout, strategies to enhance formal positive feedback are also warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".